[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

src/emcee/ensemble.py

@@ -35,17 +35,20 @@ def infer_dict_mapping(state):
 
 def array_to_dict(ary, param_slice_shape):
     return {
-        key: ary[:, slc].reshape((-1,)+shape)
+        key: ary[:, slc].reshape((-1,) + shape)
         for key, (slc, shape) in param_slice_shape.items()
     }
 
 
 def array_to_list_of_dicts(ary, param_slice_shape):
     # reshape adds a small amount of overhead; don't do it unless necessary
-    return [{
-        key: ary_i[slc].reshape(shape) if len(shape) > 1 else ary_i[slc]
-        for key, (slc, shape) in param_slice_shape.items()
-    } for ary_i in ary]
+    return [
+        {
+            key: ary_i[slc].reshape(shape) if len(shape) > 1 else ary_i[slc]
+            for key, (slc, shape) in param_slice_shape.items()
+        }
+        for ary_i in ary
+    ]
 
 
 def collapse_and_hstack(values, nwalkers=None):
@@ -199,15 +202,15 @@ def __init__(
             if isinstance(parameter_names, Sequence):
                 if len(parameter_names) != ndim:
                     raise ValueError(
-                        f"`parameter_names` does not specify {ndim} names")
+                        f"`parameter_names` does not specify {ndim} names"
+                    )
                 parameter_names = dict(zip(parameter_names, range(ndim)))
 
             indices = np.arange(ndim)
 
             try:
                 index_map = {
-                    key: indices[slc]
-                    for key, slc in parameter_names.items()
+                    key: indices[slc] for key, slc in parameter_names.items()
                 }
                 indexed = collapse_and_hstack(index_map.values())
             except IndexError as err:
@@ -330,7 +333,8 @@ def sample(
             _state = {key: val[0] for key, val in initial_state.items()}
             self.param_slice_shape = infer_dict_mapping(_state)
             initial_state = collapse_and_hstack(
-                initial_state.values(), self.nwalkers)
+                initial_state.values(), self.nwalkers
+            )
 
         state = State(initial_state, copy=True)
         state_shape = np.shape(state.coords)